Title or Caption:An Interaction-driven Approach for Inferring the Polarity of Collaborations in Wikipedia and Political Preferences on Twitter

Author or Creator:Makazhanov, Aibek

Language:English

Subject keyword(s):wikipediatwittersocial interactions

Date Accepted:Nov 30, 2012 9:57 AM

Type of Item:Thesis

Language:English

Format(s):Adobe PDF

File size(s):636997 bytes

Abstract:In this thesis we explore interactions of users of two major information sources, namely Wikipedia and Twitter. In particular, we show that revision histories of Wikipedia articles contain interaction patterns which can be used to build collaboration profiles of editors. Such profiles can be classified as positive or negative, corresponding to productive or counter-productive collaboration. Additionally, profiles can be applied to a number of related tasks, such as predicting votes in Wikipedia administrator elections or detecting controversial articles. We further extend the ideas behind collaboration profiles and adapt the approach to the problem of predicting political preference of Twitter users. By considering tweeting on a party-specific topic as a form of user-party interaction, we show that a record of such interactions, produced by a user during an election campaign, can be used to predict her political preference. Additionally, we analyze how does predicted political preference of different groups of users change over time. Our results suggest that politically active users are less likely to change their preference during the course of an election campaign.